Uncertainty matters when checking fairness of machine learning models
The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
Machine Learning
Summary
Machine learning models are used in important areas like healthcare and law enforcement, where fairness is crucial. The paper explains that checking if these models are fair often ignores how uncertain we are about the model choices themselves. The authors discuss methods to measure this uncertainty using statistical and Bayesian approaches. This helps give a clearer picture of how confident we can be that models treat people fairly, based on both simulated and real data examples.
What this means in practice
- •For clinical data teams: Evaluate how uncertain fairness assessments are in healthcare prediction models to ensure trustworthy clinical decisions.
- •For social media platform engineers: Assess uncertain bias in content recommendation systems to improve fairness across diverse user groups.
Authors
Francesca Panero, Ernst C. Wit, Marco Scutari
Abstract
Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their outputs are biased against disadvantaged groups or individuals is crucial to ensuring they are fair and allowing their use in such settings. A rigorous risk assessment of possible fairness violations requires quantifying the uncertainty associated with selecting and estimating such models. Yet, this is rarely done in the literature, which focuses on identifying a single model with a suitable trade-off between predictive accuracy and fairness. In this paper, we move beyond point estimation and discuss frequentist and Bayesian approaches to uncertainty quantification for fair machine learning, with practical examples and implications for simulated and real data.